Engineering Commons^™

Decoding Complexity In Metabolic Networks Using Integrated Mechanistic And Machine Learning Approaches, Tolutola Timothy Oyetunde

McKelvey School of Engineering Graduate Student Theses & Dissertations

How can we get living cells to do what we want? What do they actually ‘want’? What ‘rules’ do they observe? How can we better understand and manipulate them? Answers to fundamental research questions like these are critical to overcoming bottlenecks in metabolic engineering and optimizing heterologous pathways for synthetic biology applications. Unfortunately, biological systems are too complex to be completely described by physicochemical modeling alone.

In this research, I developed and applied integrated mechanistic and data-driven frameworks to help uncover the mysteries of cellular regulation and control. These tools provide a computational framework for seeking answers to pertinent biological …

Blood-Based Biomarkers For Predicting The Risk For Five-Year Incident Coronary Heart Disease In The Framingham Heart Study Via Machine Learning, Meeshanthini V. Dogan, Steven R.H. Beach, Ronald L. Simons, Amaury Lendasse, Brandan Penaluna, Robert A. Philibert

Engineering Management and Systems Engineering Faculty Research & Creative Works

An Improved Approach for Predicting the Risk for Incident Coronary Heart Disease (CHD) Could Lead to Substantial Improvements in Cardiovascular Health. Previously, We Have Shown that Genetic and Epigenetic Loci Could Predict CHD Status More Sensitively Than Conventional Risk Factors. Herein, We Examine Whether Similar Machine Learning Approaches Could Be Used to Develop a Similar Panel for Predicting Incident CHD. Training and Test Sets Consisted of 1180 and 524 Individuals, respectively. Data Mining Techniques Were Employed to Mine for Predictive Biosignatures in the Training Set. an Ensemble of Random Forest Models Consisting of Four Genetic and Four Epigenetic Loci Was …

Generating Exploration Mission-3 Trajectories To A 9:2 Nrho Using Machine Learning, Esteban Guzman

Master's Theses

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on …

Machine Learning Methods To Map Stabilizer Effectiveness Based On Common Soil Properties, Amit Gajurel

Boise State University Theses and Dissertations

Unconfined compressive strength (UCS) has been widely used as one of the primary criteria for the selection of optimum type and amount of chemical stabilizer for subgrade/base stabilization. Guidelines established by various state and federal agencies aid in selecting these optimum values by recommending an initial type and amount based on a wide range of soil index properties. A significant number of laboratory trials have to be done to establish the optimum type and amount of stabilizer for a given target strength. This process takes a copious amount of time, money, and the workforce. In addition to that, the finite …

Amplifying The Prediction Of Team Performance Through Swarm Intelligence And Machine Learning, Erick Michael Harris

Master's Theses

Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to …

Landmine Detection Using Semi-Supervised Learning., Graham Reid

Electronic Theses and Dissertations

Landmine detection is imperative for the preservation of both military and civilian lives. While landmines are easy to place, they are relatively difficult to remove. The classic method of detecting landmines was by using metal-detectors. However, many present-day landmines are composed of little to no metal, necessitating the use of additional technologies. One of the most successful and widely employed technologies is Ground Penetrating Radar (GPR). In order to maximize efficiency of GPR-based landmine detection and minimize wasted effort caused by false alarms, intelligent detection methods such as machine learning are used. Many sophisticated algorithms are developed and employed to …

Computational Support For Predicting Requirement Change Volatility In Complex System Design, Phyo Htet Hein

Theses and Dissertations

Requirements play critical role in the design process as objective statements of stakeholders’ expectations. Design process is iterative, and requirements are also constantly changed and updated to reflect stakeholders’ expectations, design changes, regulations, resource limitations, etc. Managing requirement change is one of the most important requirement management tasks. Since requirements are driving factors in product development from initial concept development stage to final production, mismanaged requirement changes can adversely affect project health leading to monetary and time losses. The ability to assess a requirement change and predict its propagation early in the design process will enable engineers to make informed …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Crash Severity Analysis Of Rear-End Crashes In California Using Statistical And Machine Learning Classification Methods, Alidad Ahmadi, Arash Jahangiri, Vincent Berardi, Sahar Ghanipoor Machiani

Psychology Faculty Articles and Research

Investigating drivers’ injury level and detecting contributing factors that aggravate the damage level imposed on drivers and vehicles is a critical subject in the field of crash analysis. In this study, a comprehensive vehicle-by-vehicle crash data set is developed by integrating 5 years of data from California crash, vehicles involved, and road databases. The data set is used to model the severity of rear-end crashes for comparing three analytic techniques: multinomial logit, mixed multinomial logit, and support vector machine (SVM). The results of the crash severity models and the role of contributing factors to the severity outcome of rear-end crashes …

Machine Learning Tools For Optimization Of Fuel Consumption At Signalized Intersections In Connected/Automated Vehicles Environment, Saleh Ragab Mousa

LSU Doctoral Dissertations

Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. …

Domain-Specific Use Cases For Knowledge-Enabled Social Media Analysis, Soon Jye Kho, Swati Padhee, Goonmeet Bajaj, Krishnaprasad Thirunarayan, Amit Sheth

Publications

No abstract provided.

Refining Statistical Magnesium Models Via Machine Learning, Andrew Orme, Dr. David Fullwood

Journal of Undergraduate Research

Magnesium is a potential replacement for steels and aluminum in strength applications. Despite desirable strength and weight properties, magnesium is costly to manufacture. To reduce manufacturing costs, extensive research has been done on is a phenomenon called twinning, where a large group of magnesium atoms collectively reorient from a base orientation to a new orientation. This reorientation caused by twinning has the potential to enable easier material deformation, allowing for less costly manufacturing. Our research group pursued a novel approach to twinning research by using data mining and machine learning algorithms. Data collected from samples of magnesium using a scanning …

Micro-Manipulation Using Learned Model, Matthew A. Lyng, Benjamin V. Johnson, David J. Cappelleri

The Summer Undergraduate Research Fellowship (SURF) Symposium

Microscale devices can be found in applications ranging from sensors to structural components. The dominance of surface forces at the microscale hinders the assembly processes through nonlinear interactions that are difficult to model for automation, limiting designs of microsystems to primarily monolithic structures. Methods for modeling surface forces must be presented for viable manufacturing of devices consisting of multiple microparts. This paper proposes the implementation of supervised machine learning models to aid in automated micromanipulation tasks for advanced manufacturing applications. The developed models use sets of training data to implicitly model surface interactions and predict end-effector placement and paths that …

Supervised Sparse Learning With Applications In Bioinformatics, Kin Ming Puk

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

In machine learning and mathematical optimization, sparse learning is the use of mathematical norms such as L1-norm, group norm and L21-norm in order to seek a trade-off between the goodness-of-fit measure and sparsity of the result. Sparsity of result leads to a parsimonious learning model - in other words, only few features from the data matrix are required to build the learning model and for further interpretation. The motivations of employing sparse learning in bioinformatics are two-fold: firstly, a parsimonious learning model enhances the explanatory power; and secondly, a parsimonious model generally allows better prediction and generalizes better to new …

Inverse Analysis Of Heat Generating Body For Safety Applications, Sandeep Patil

Mechanical and Aerospace Engineering Dissertations - Archive

Inverse thermal analysis and its applications have been applied to numerous fields of science and engineering. Historically during the 1950's and early 1960's, space programs played a significant role in the advancement of solution techniques for Inverse Heat transfer Problems(IHTP). It was applied to measure the surface temperature of thermal shield of a space vehicle during its re-entry into atmosphere. Inverse analysis was also used in the estimation of thermo-physical properties of the shield at high operating temperatures. Besides thermal application, inverse technique was also used in other engineering applications such as estimation of alloy specification, design of a shape …

Machine Learning For Internet Of Things Data Analysis: A Survey, Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, Amit Sheth

Publications

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the …

Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren

Faculty Publications

This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and …

Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn

Electrical & Computer Engineering Theses & Dissertations

The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify …

Prediction Of Drugs To Be Prescribed To A Diabetic, Ojasva Jain

Manipal Journal of Science and Technology

The purpose of this research is to review the outcome measures used to evaluate a prevailing disease, in this case, diabetes and make recommendations for the incoming future patients.

N-Slope: A One-Class Classification Ensemble For Nuclear Forensics, Justin Kehl

Master's Theses

One-class classification is a specialized form of classification from the field of machine learning. Traditional classification attempts to assign unknowns to known classes, but cannot handle novel unknowns that do not belong to any of the known classes. One-class classification seeks to identify these outliers, while still correctly assigning unknowns to classes appropriately. One-class classification is applied here to the field of nuclear forensics, which is the study and analysis of nuclear material for the purpose of nuclear incident investigations. Nuclear forensics data poses an interesting challenge because false positive identification can prove costly and data is often small, high-dimensional, …

An Investigation Of The Cortical Learning Algorithm, Anthony C. Samaritano

Theses and Dissertations

Pattern recognition and machine learning fields have revolutionized countless industries and applications from biometric security to modern industrial assembly lines. The fields continue to accelerate as faster, more efficient processing hardware becomes commercially available. Despite the accelerated growth of the pattern recognition and machine learning fields, computers still are unable to learn, reason, and perform rudimentary tasks that humans and animals find routine. Animals are able to move fluidly, understand their environment, and maximize their chances of survival through adaptation - animals demonstrate intelligence. A primary argument in this thesis that we have not yet achieved a level of intelligence …

Design Of A Distributed Real-Time E-Health Cyber Ecosystem With Collective Actions: Diagnosis, Dynamic Queueing, And Decision Making, Yanlin Zhou

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we develop a framework for E-health Cyber Ecosystems, and look into different involved actors. The three interested parties in the ecosystem including patients, doctors, and healthcare providers are discussed in 3 different phases. In Phase 1, machine-learning based modeling and simulation analysis is performed to remotely predict a patient's risk level of having heart diseases in real time. In Phase 2, an online dynamic queueing model is devised to pair doctors with patients having high risk levels (diagnosed in Phase 1) to confirm the risk, and provide help. In Phase 3, a decision making paradigm is proposed …

Experiences Building, Training, And Deploying A Chatbot In An Academic Library, David Meincke

Library Staff Publications

No abstract provided.

A Two Stage Event Based Data Driven Controller For Improved Grasping Of An Artificial Hand, Christopher Abrego

Mechanical and Aerospace Engineering Dissertations - Archive

The human hand is one of the greatest (if not the greatest) tool known to mankind for grasping objects. So much so, that researchers have been investigating the development of artificial biomimetic hands in an effort to mimic their functionality and dexterity with the indent to apply the technology to various robotic platforms; ranging from end effectors for industrial pick-and-place robotics, to upper-limb prosthetics, to humanoids. There are certain features that make this endeavor challenging such as the mechanical design, actuation and sensorization, and functionality as well as the interaction from both the view point of interacting with an end …

On The Development And Integration Of Pneumatic Extrusion Module And A Methodology To Identify Process Parameters For Additive Manufacturing Using Machine Learning, Kashish Dhal

Mechanical and Aerospace Engineering Theses - Archive

Commonly used additive manufacturing platforms have a single extrusion module based on Fused Filament Fabrication (FFF) and their processing software generates G-Codes for this FFF module using defined process parameters. These platforms and software do not accommodate different processing modules such as viscous extruders or Direct Ink Writing (DIW). This research is focused on the development of a Pneumatic Extrusion Module (PEM) capable of dispensing viscous materials such as gels or slurries controlled through a digital pneumatic valve. A PEM is developed, integrated and its performance is evaluated on a multi-modality additive manufacturing platform in the MARS Lab. The operation …

Horse Racing Prediction Using Graph-Based Features., Mehmet Akif Gulum

Electronic Theses and Dissertations

This thesis presents an applied horse racing prediction using graph based features on a set of horse races data. We used artificial neural network and logistic regression models to train then test to prediction without graph based features and with graph based features. This thesis can be explained in 4 main parts. Collect data from a horse racing website held from 2015 to 2017. Train data to using predictive models and make a prediction. Create a global directed graph of horses and extract graph-based features (Core Part) . Add graph based features to basic features and train to using same …

End-To-End Learning Framework For Circular Rna Classification From Other Long Non-Coding Rnas Using Multi-Modal Deep Learning., Mohamed Chaabane

Electronic Theses and Dissertations

Over the past two decades, a circular form of RNA (circular RNA) produced from splicing mechanism has become the focus of scientific studies due to its major role as a microRNA (miR) ac tivity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is a vital operation for continued comprehension of their biogenesis and purpose. Prediction of circular RNA can be achieved by first distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs (lncRNAs), and finally pre dicting circular RNAs from other lncRNAs. However, available tools to distinguish circular …

Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard

Electronic Theses and Dissertations

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A …

A Framework For Cardio-Pulmonary Resuscitation (Cpr) Scene Retrieval From Medical Simulation Videos Based On Object And Activity Detection., Anju Panicker Madhusoodhanan Sathik

Electronic Theses and Dissertations

In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented …

A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau

Research Collection School Of Computing and Information Systems

The age of artificial intelligence is here! Artificial Intelligence, robotics, machine learning, and automation are impacting the field of marketing and sales in an unprecedented way. In this study, the qualitative research methodology will be used to better understand the revolution and evolution of marketing and sales field in the AI age. Multiple case studies will be performed in various marketing and sales units in different organizations. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the changes in marketing and sales functionalities and job markets as AI …